Shot boundary detection via adaptive low rank and svd-updating

Bendraou, Youssef; Essannouni, Fedwa; Aboutajdine, Driss; Mashaly, Ahmed S.

doi:10.1016/j.cviu.2017.06.003

Cited by 23 publications

(7 citation statements)

References 18 publications

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“…To prove the efficiency of the proposed algorithm, additional comparisons are performed between the proposed algorithm and different existing algorithms. These algorithms are: SBD algorithm concatenated block-based SBD algorithm (CBB-SBD) [54], Walsh-Hadamard transform-based SBD algorithm (WHT-SBD) [11], SBD algorithm based on convolutional neural networks (CNN-SBD) [55], and SBD using Non-Subsampled Contourlet Transform (NSCT-SBD) [14].…”

Section: Performance Evaluation Of the Proposed Sbd Algorithmmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.

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Section: Performance Evaluation Of the Proposed Sbd Algorithmmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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“…To evaluate the performance of the proposed TVS algorithm, the proposed algorithm is compared to the stateof-the-art algorithms. The state-of-the-art algorithm are: TVS algorithm based on Non-Subsampled Contourlet Transform and SVM (NSCT) [23], TVS algorithm based on Walsh-Hadamard transform (WHT) [22], TVS algorithm based on concatenated block based histograms (CBBH) [61], and TVS algorithm based on orthogonal polynomial and FBP (OPFBP) [12]. The comparison is presented in TABLE 8 and TABLE 9 in terms of computation cost and accuracy.…”

Section: Resultsmentioning

confidence: 99%

Fast Temporal Video Segmentation Based on Krawtchouk-Tchebichef Moments

et al. 2020

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With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1score are within the ranges (1.31 -2.58), (1.53 -4.28), and (1.41 -3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time.

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“…ASIFT is another enhanced version of SIFT [10], which deal with the view change in image matching. Singular Value Decomposition (SVD) updating is used with adaptive feature extraction in [11]. In order to identify hard cuts, double thresholding technique is used as classifier.…”

Section: Fig 1 General Structure Of a Videomentioning

confidence: 99%

An Efficient and Robust Temporal Video Segmentation

Jose*¹,

Rajkumar²

2019

IJEAT

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Temporal video segmentation is the primary step of content based video retrieval. The whole processes of video management are coming under the focus of content based video retrieval, which includes, video indexing, video retrieval, and video summarization etc. In this paper, we proposed a computationally efficient and discriminating shot boundary detection method, which uses a local feature descriptor named local Contrast and Ordering (LCO) for feature extraction. The results of the experiments, which are conducted on the video dataset TRECVid, analyzed and compared with some existing shot boundary detection methods. The proposed method has given a promising result, even in the cases of illumination changes, rotated images etc.

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Shot boundary detection via adaptive low rank and svd-updating

Cited by 23 publications

References 18 publications

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

Fast Temporal Video Segmentation Based on Krawtchouk-Tchebichef Moments

An Efficient and Robust Temporal Video Segmentation

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